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相关概念视频

Volatilization01:10

Volatilization

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Volatilization gravimetry is an analytical technique that measures the mass lost due to the volatilization of the substance. This technique is used to estimate the amount of volatile material in a sample. To perform this method, heat a known amount of the sample to a high temperature in a crucible or other suitable vessel. The volatile substance in the sample evaporates, and the vapor is completely expelled from the crucible either by heating the sample or bubbling a stream of inert gas through...
322

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相关实验视频

Updated: May 13, 2025

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
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可解释的深度学习,从挥发性有机化合物分析中预测鱼的地理起源.

Xuming Kang1, Zhijun Tan1,2, Yanfang Zhao1

  • 1Key Laboratory of Testing and Evaluation for Aquatic Product Safety and Quality, Ministry of Agriculture and Rural Affairs, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China.

Foods (Basel, Switzerland)
|April 16, 2025
PubMed
概括

现在可以使用挥发性有机化合物 (VOC) 和可解释的深度学习来识别海藻的起源. 这种方法可以准确地追踪鱼的踪迹.

关键词:
这就是 SHAP SHAP 的意思.可以解释的深度学习.地理来源的地理来源.凯尔普 凯尔普 凯尔普挥发性有机化合物 挥发性有机化合物

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科学领域:

  • 食品科学 食品科学 食品科学
  • 分析化学 分析化学
  • 计算生物学 计算生物学

背景情况:

  • 消费者选择越来越多地取决于海藻的来源,而不仅仅是风味和营养.
  • 关于用挥发性有机化合物 (VOCs) 来追溯的起源,目前的研究有限.
  • 深度学习的可追溯性应用受到其"黑子"性质的阻碍.

研究的目的:

  • 开发一种使用VOC分析识别海藻来源的方法.
  • 应用可解释的深度学习来实现透明和准确的可追溯性.
  • 增强消费者信任和实际应用海藻来源信息.

主要方法:

  • 在海藻样本中使用气色谱-离子流动性光谱学 (GC-IMS) 识别了115种不同的VOC.
  • 开发了一个一维卷积神经网络 (1D-CNN) 模型,包含107个关键的VOC.
  • 为了模型的可解释性,利用了夏普利添加式解释 (SHAP).

主要成果:

  • 实现了完美的性能指标:100%的准确性,精度,回忆,F1得分和1.0AUC.
  • 根据VOC档案,成功辨认了鱼的起源.
  • 确定了特定的VOC,如1-Octen-3-ol-M和 (+) -limonene作为关键指标.

结论:

  • 可解释的深度学习通过VOC分析有效地追踪海藻的起源.
  • 该模型为地理可追溯性提供了准确和可解释的结果.
  • 这种方法可以提高消费者的信心,并促进现实世界鱼来源验证.